Automatic Human Body Feature Extraction and Size Measurement by Random Forest Regression Analysis of Geodesics Distance

2017 ◽  
Author(s):  
Xiaohui Tan ◽  
Xiaoyu Peng ◽  
Liwen Liu ◽  
Qing Xia
Keyword(s):  
Feature Extraction ◽  
Regression Analysis ◽  
Random Forest ◽  
Human Body ◽  
Size Measurement ◽  
Random Forest Regression

Determination of Chlorpyrifos in Pears by Raman Spectroscopy with Random Forest Regression Analysis

2019 ◽  
Vol 53 (6) ◽  
pp. 821-833
Author(s):  
Xiaofan Du ◽  
Ping Wang ◽  
Lei Fu ◽  
Huifang Liu ◽  
Zhenxi Zhang ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Regression Analysis ◽  
Random Forest ◽  
Random Forest Regression

3D Digitization of Human Body Based on Checkerboard Matching

2013 ◽  
Vol 718-720 ◽  
pp. 1108-1112
Author(s):  
Jian Li ◽  
Cheng Yan Zhang ◽  
Xue Li Xu ◽  
Hai Feng Chen
Keyword(s):  
Feature Extraction ◽  
Body Size ◽  
Human Body ◽  
Measurement Method ◽  
Stereo Matching ◽  
Low Cost ◽  
Size Measurement ◽  
Body Images ◽  
General Application ◽  
3D Digitization

A body-size measurement method based on checkerboard matching is proposed. First, calibrated cameras are used to acquire two body images after projecting chess boards on human body with projector. Then, the parallax of the two images is got by feature extraction and stereo matching. Finally, we can calculate the 3D coordinates of the human body according to the principle of binocular vision to complete the acquisition of body size. The result shows that measurement error is ± 4%. This study can measure automatically and improve precision compared with traditional methods while it has low-cost, simple operation compared with the non-contact measurement. And the results accuracy can meet its general application in practice.

scholarly journals RANDOM FOREST REGRESSION FOR THE ESTIMATION OF LEAF AREA INDEX OF OKRA CROP USING GROUND BASED BISTATIC SCATTEROMETER

2018 ◽  
Vol XLII-5 ◽  
pp. 719-725
Author(s):  
S. A. Yadav ◽  
R. Prasad ◽  
A. K. Vishwakarma ◽  
V. P. Yadav
Keyword(s):  
Regression Analysis ◽  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Data Sets ◽  
Angle Of Incidence ◽  
Random Forest Regression ◽  
Area Index ◽  
Scattering Coefficients

<p><strong>Abstract.</strong> The specular bistatic scattering mechanism of Okra's crop was analyzed using dual polarized ground based bistatic scatterometer system at X, C, and L bands in the specular direction with the azimuthal angle(&amp;theta;<span class="thinspace"></span>=<span class="thinspace"></span>0&amp;deg;). An outdoor Okra crop bed of area 10<span class="thinspace"></span>&amp;times;<span class="thinspace"></span>10<span class="thinspace"></span>m<sup>2</sup> was specially prepared for the estimation of leaf area index (LAI) at HH and VV polarizations over the angular range of incidence angle 20&amp;deg; to 60&amp;deg; at steps of 10&amp;deg;. The regression analysis was done between bistatic specular scattering coefficients and crop biophysical parameter at X, C, and L bands for HH and VV polarization at different angle of incidence to determine the optimum parameters of bistatic scatterometer system. The linear regression analysis showed the high correlation at 40&amp;deg; angle of incidence for all bands and polarizations for the Okra crop. The computed scattering coefficients and measured LAI of Okra crop for the seven growth stages at 40&amp;deg; angle of incidence were interpolated into 61 data sets. The data sets were divided into input, validation and testing for the training and testing of the developed random forest regression (RF) model for the estimation of LAI for Okra crop. The estimated values of LAI of Okra crop, by the developed RF regression model, were found more closer to the observed values at X band for VV polarization with coefficient of determination (R<sup>2</sup><span class="thinspace"></span>=<span class="thinspace"></span>0.928) and low root mean square error (RMSE<span class="thinspace"></span>=<span class="thinspace"></span>0.260<span class="thinspace"></span>m<sup>2</sup>/m<sup>2</sup>) in comparison to C and L bands.</p>

scholarly journals Automatic human body feature extraction and personal size measurement

2018 ◽  
Vol 47 ◽  
pp. 9-18 ◽  
Author(s):  
Tan Xiaohui ◽  
Peng Xiaoyu ◽  
Liu Liwen ◽  
Xia Qing
Keyword(s):  
Feature Extraction ◽  
Human Body ◽  
Size Measurement

scholarly journals Earthquake Prediction using Seismic Information

2020 ◽  
pp. 554-558
Author(s):  
Madhushree N ◽  
R. Thirukkumaran
Keyword(s):  
Data Mining ◽  
Feature Extraction ◽  
Random Forest ◽  
Earthquake Prediction ◽  
Future Time ◽  
Random Forest Regression ◽  
Prediction Rate ◽  
Challenging Research ◽  
Natural Calamity ◽  
Feature Extraction Technique

Earhquake is one of the most hazardous, devasting natural calamity and yet a very least predictable natural disaster that occur. Prediction of earthquake has been a challenging research for many researchers. With the increasing amount of earthquake dataset collected, many researchers try to solve the task of predicting the earthquake in future time. Even though many data mining techniques are been used, the prediction rate is not still accurate due to lack of feature extraction technique. The proposed methodology enhance the performance of earthquake prediction. As obtained precursory pattern features along with Random forest regression is used to get prediction of the magnitude of future earthquakes.

scholarly journals UHPLC–MS/MS-Based Nontargeted Metabolomics Analysis Reveals Biomarkers Related to the Freshness of Chilled Chicken

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1326
Author(s):  
Tao Zhang ◽  
Shanshan Zhang ◽  
Lan Chen ◽  
Hao Ding ◽  
Pengfei Wu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Random Forest ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Detection Methods ◽  
Metabolic Profiles ◽  
Stepwise Multiple Linear Regression ◽  
Random Forest Regression ◽  
Metabolic Biomarkers

To identify metabolic biomarkers related to the freshness of chilled chicken, ultra-high-performance liquid chromatography–mass spectrometry (UHPLC–MS/MS) was used to obtain profiles of the metabolites present in chilled chicken stored for different lengths of time. Random forest regression analysis and stepwise multiple linear regression were used to identify key metabolic biomarkers related to the freshness of chilled chicken. A total of 265 differential metabolites were identified during storage of chilled chicken. Of these various metabolites, 37 were selected as potential biomarkers by random forest regression analysis. Receiver operating characteristic (ROC) curve analysis indicated that the biomarkers identified using random forest regression analysis showed a strong correlation with the freshness of chilled chicken. Subsequently, stepwise multiple linear regression analysis based on the biomarkers identified by using random forest regression analysis identified indole-3-carboxaldehyde, uridine monophosphate, s-phenylmercapturic acid, gluconic acid, tyramine, and Serylphenylalanine as key metabolic biomarkers. In conclusion, our study characterized the metabolic profiles of chilled chicken stored for different lengths of time and identified six key metabolic biomarkers related to the freshness of chilled chicken. These findings can contribute to a better understanding of the changes in the metabolic profiles of chilled chicken during storage and provide a basis for the further development of novel detection methods for the freshness of chilled chicken.

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

Sign in / Sign up

Export Citation Format

Share Document